Screening for Dementia and Cognitive Decline in Adults With Down Syndrome
Bibliographic record
Abstract
OBJECTIVE: The aim was to examine the psychometric properties of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) as a diagnostic tool to screen for dementia in aging individuals with Down syndrome (DS). METHODS: This was a cross-sectional study of 92 individuals with DS 30 y or above of age) evaluated with the IQCODE. Using the informant questionnaire of the Cambridge Examination for Mental Disorders of Older People with Down's Syndrome and Others with Intellectual Disabilities, we divided the subjects into 3 diagnostic groups: stable cognition; prodromal dementia; and dementia. The ability of the IQCODE to discriminate between diagnostic groups was analyzed by calculating the areas under the receiver operator characteristic curves (AUCs). RESULTS: The optimal IQCODE cutoffs were 3.14 for dementia versus stable cognition (AUC=0.993; P<0.001) and 3.11 for prodromal dementia+dementia versus stable cognition (AUC=0.975; P<0.001), with sensitivity/specificity/accuracy of 100%/96.8%/97.3%, and 93.3%/91.9%/92.4%, respectively. The IQCODE showed a weak-to-moderate correlation with cognitive performance (P<0.05). CONCLUSION: The IQCODE is a useful tool to screen for cognitive decline in individuals with DS and is suitable for use in a primary care setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".